Mathematical support and software systems for spare parts management of grain harvesting machinery

Abstract

Agricultural production is characterized by the seasonality of technological operations, the ability to carry out which in a strictly set time depends on many factors of the external environment and the reliability of agricultural machinery. The harvest of grain and other crops by combines occupies a special place among field works. Operative carrying out of these works without down time of the grain-harvesting techniques provides the minimum losses of the agricultural production. It is possible to minimize downtime of harvesters due to technical reasons if the enterprise has at its warehouse a seasonal reserve of spare parts that are in great demand during harvesting works. The methods of reserve calculation should consider not only the reliability of some harvester parts, but the cost damage of harvester downtime and extra costs of spare parts purchase and storage. With such a large number of external factors, traditional methods are difficult to calculate. With the development of computer technology, the methods of evolutionary calculations have been widely used, in particular, genetic algorithms that allow creating self-adjusting models capable of analysing the examined indicators for the past years, reacting to the changes of current external conditions, and making short-term forecasts of the values of optimised variables. The purpose of this research was to develop a genetic algorithm and software that will enable us to meet the seasonal demand for spare parts from combine harvesters. The software allowed us to identify 66 of the most in-demand items out of 2800 warehouse items that should be reserved for the season of harvesting crops. The efficiency of the proposed solutions is proved by the reduction of the downtime of combines due to technical reasons by 37% and the increase of their shift productivity by 11.4%; at the same time, the combine reliability index, the operational readiness factor, increases by 4.38%.